Tracing and enhancing serendipitous learning with viewpointS
This is a position paper describing the author's views on a potential new research direction for assessing, constructing and exploiting brainfounded models of learning of individual as well as collective humans. The recent approach – called ViewpointS – aiming to unify the Semantic and the Social Web, data mining included, by means of a simple “subjective” primitive – the viewpoint - denoting proximity among elements of the world, seems to offer a promising context of innovative empirical research in modeling human learning less constrained with respect to the previous three other ones. Within this context, a few phenomena of serendipitous learning have been simulated, showing that the process of collective construction of knowledge during free navigation may offer interesting side effects of informal, serendipitous knowledge acquisition and learning. We envision therefore an extension of the modeling functions within ViewpointS by adding measures of the emotions and mental states as acquired during experimental sessions. These brain-related components may in a first phase allow to describe and classify models in order to understand the relations among knowledge structures and mental states. Subsequently, more predictive experiments may be envisaged. These may allow to forecast the acquisition of knowledge as well as sentiment from previous events during interactions. We are convinced that useful applications may range, for instance, from Tutoring, to Health, to consensus formation in Politics at very low investment costs as the experimental set up consists of minimal extensions of the Web.
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dig-cirad-fr-5854732021-01-04T12:15:44Z http://agritrop.cirad.fr/585473/ http://agritrop.cirad.fr/585473/ Tracing and enhancing serendipitous learning with viewpointS. Cerri Stefano A., Lemoisson Philippe. 2017. In : Brain Function Assessment in Learning, First International Conference, BFAL 2017, Patras, Greece, September 24-25, 2017, Proceedings. Frasson Claude (ed.), Kostopoulos George (ed.). Cham : Springer, 36-47. (Lecture Notes in Artificial Intelligence, 10512) ISBN 978-3-319-67614-2 International conference on Brain Function Assessment in Learning (BFAL 2017), Patras, Grèce, 24 Septembre 2017/25 Septembre 2017.https://doi.org/10.1007/978-3-319-67615-9_3 <https://doi.org/10.1007/978-3-319-67615-9_3> Researchers Tracing and enhancing serendipitous learning with viewpointS Cerri, Stefano A. Lemoisson, Philippe eng 2017 Springer Brain Function Assessment in Learning, First International Conference, BFAL 2017, Patras, Greece, September 24-25, 2017, Proceedings 000 - Autres thèmes U30 - Méthodes de recherche C30 - Documentation et information This is a position paper describing the author's views on a potential new research direction for assessing, constructing and exploiting brainfounded models of learning of individual as well as collective humans. The recent approach – called ViewpointS – aiming to unify the Semantic and the Social Web, data mining included, by means of a simple “subjective” primitive – the viewpoint - denoting proximity among elements of the world, seems to offer a promising context of innovative empirical research in modeling human learning less constrained with respect to the previous three other ones. Within this context, a few phenomena of serendipitous learning have been simulated, showing that the process of collective construction of knowledge during free navigation may offer interesting side effects of informal, serendipitous knowledge acquisition and learning. We envision therefore an extension of the modeling functions within ViewpointS by adding measures of the emotions and mental states as acquired during experimental sessions. These brain-related components may in a first phase allow to describe and classify models in order to understand the relations among knowledge structures and mental states. Subsequently, more predictive experiments may be envisaged. These may allow to forecast the acquisition of knowledge as well as sentiment from previous events during interactions. We are convinced that useful applications may range, for instance, from Tutoring, to Health, to consensus formation in Politics at very low investment costs as the experimental set up consists of minimal extensions of the Web. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/585473/1/_440917_1_En_3_Chapter_Author.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-3-319-67615-9_3 10.1007/978-3-319-67615-9_3 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=219585 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-67615-9_3 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-3-319-67615-9_3 |
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000 - Autres thèmes U30 - Méthodes de recherche C30 - Documentation et information 000 - Autres thèmes U30 - Méthodes de recherche C30 - Documentation et information Cerri, Stefano A. Lemoisson, Philippe Tracing and enhancing serendipitous learning with viewpointS |
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This is a position paper describing the author's views on a potential new research direction for assessing, constructing and exploiting brainfounded models of learning of individual as well as collective humans. The recent approach – called ViewpointS – aiming to unify the Semantic and the Social Web, data mining included, by means of a simple “subjective” primitive – the viewpoint - denoting proximity among elements of the world, seems to offer a promising context of innovative empirical research in modeling human learning less constrained with respect to the previous three other ones. Within this context, a few phenomena of serendipitous learning have been simulated, showing that the process of collective construction of knowledge during free navigation may offer interesting side effects of informal, serendipitous knowledge acquisition and learning. We envision therefore an extension of the modeling functions within ViewpointS by adding measures of the emotions and mental states as acquired during experimental sessions. These brain-related components may in a first phase allow to describe and classify models in order to understand the relations among knowledge structures and mental states. Subsequently, more predictive experiments may be envisaged. These may allow to forecast the acquisition of knowledge as well as sentiment from previous events during interactions. We are convinced that useful applications may range, for instance, from Tutoring, to Health, to consensus formation in Politics at very low investment costs as the experimental set up consists of minimal extensions of the Web. |
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conference_item |
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000 - Autres thèmes U30 - Méthodes de recherche C30 - Documentation et information |
author |
Cerri, Stefano A. Lemoisson, Philippe |
author_facet |
Cerri, Stefano A. Lemoisson, Philippe |
author_sort |
Cerri, Stefano A. |
title |
Tracing and enhancing serendipitous learning with viewpointS |
title_short |
Tracing and enhancing serendipitous learning with viewpointS |
title_full |
Tracing and enhancing serendipitous learning with viewpointS |
title_fullStr |
Tracing and enhancing serendipitous learning with viewpointS |
title_full_unstemmed |
Tracing and enhancing serendipitous learning with viewpointS |
title_sort |
tracing and enhancing serendipitous learning with viewpoints |
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Springer |
url |
http://agritrop.cirad.fr/585473/ http://agritrop.cirad.fr/585473/1/_440917_1_En_3_Chapter_Author.pdf |
work_keys_str_mv |
AT cerristefanoa tracingandenhancingserendipitouslearningwithviewpoints AT lemoissonphilippe tracingandenhancingserendipitouslearningwithviewpoints |
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1758025479339638784 |